OpenRaiser/Intern-Atlas huggingface dataset:
On the Design of Multi-Dimensional Irregular Repeat-Accumulate Lattice Codes
Venue: arXiv cs.IT 2017
Year: 2017

Most multi-dimensional (more than two dimensions) lattice partitions only form additive quotient groups and lack multiplication operations. This prevents us from constructing lattice codes based on multi-dimensional lattice partitions directly from non-binary linear codes over finite fields. In this paper, we design lattice codes from Construction A lattices where the underlying linear codes are non-binary irregular repeat-accumulate (IRA) codes. Most importantly, our codes are based on multi-dimensional lattice partitions with finite constellations. We propose a novel encoding structure that adds randomly generated lattice sequences to the encoder's messages, instead of multiplying lattice sequences to the encoder's messages. We prove that our approach can ensure that the decoder's messages exhibit permutation-invariance and symmetry properties. With these two properties, the densities of the messages in the iterative decoder can be modeled by Gaussian distributions described by a single parameter. With Gaussian approximation, extrinsic information transfer (EXIT) charts for our multi-dimensional IRA lattice codes are developed and used for analyzing the convergence behavior and optimizing the decoding thresholds. Simulation results show that our codes can approach the unrestricted Shannon limit within 0.46 dB and outperform the previously designed lattice codes with two-dimensional lattice partitions and existing lattice coding schemes for large codeword length.
Cross-Language Question Re-Ranking
Venue: arXiv cs.CL 2017
Year: 2017

We study how to find relevant questions in community forums when the language of the new questions is different from that of the existing questions in the forum. In particular, we explore the Arabic-English language pair. We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings. We observe that both approaches degrade the performance of the system when working on the translated text, especially the kernel-based system, which depends heavily on a syntactic kernel. We address this issue using a cross-language tree kernel, which compares the original Arabic tree to the English trees of the related questions. We show that this kernel almost closes the performance gap with respect to the monolingual system. On the neural network side, we use the parallel corpus to train cross-language embeddings, which we then use to represent the Arabic input and the English related questions in the same space. The results also improve to close to those of the monolingual neural network. Overall, the kernel system shows a better performance compared to the neural network in all cases.
Exploring home robot capabilities by medium fidelity prototyping
Venue: arXiv cs.RO 2017
Year: 2017

In order for autonomous robots to be able to support people's well-being in homes and everyday environments, new interactive capabilities will be required, as exemplified by the soft design used for Disney's recent robot character Baymax in popular fiction. Home robots will be required to be easy to interact with and intelligent--adaptive, fun, unobtrusive and involving little effort to power and maintain--and capable of carrying out useful tasks both on an everyday level and during emergencies. The current article adopts an exploratory medium fidelity prototyping approach for testing some new robotic capabilities in regard to recognizing people's activities and intentions and behaving in a way which is transparent to people. Results are discussed with the aim of informing next designs.
Visual and Textual Programming Languages: A Systematic Review of the Literature
Venue: arXiv cs.CY 2017
Year: 2017

It is well documented, and has been the topic of much research, that Computer Science courses tend to have higher than average drop out rates at third level. This is a problem that needs to be addressed with urgency but also caution. The required number of Computer Science graduates is growing every year but the number of graduates is not meeting this demand and one way that this problem can be alleviated is to encourage students at an early age towards studying Computer Science courses. This paper presents a systematic literature review on the role of visual and textual programming languages when learning to program, particularly as a first programming language. The approach is systematic, in that a structured search of electronic resources has been conducted, and the results are presented and quantitatively analysed. This study will give insight into whether or not the current approaches to teaching young learners programming are viable, and examines what we can do to increase the interest and retention of these students as they progress through their education.
($k$,$ε$)-Anonymity: $k$-Anonymity with $ε$-Differential Privacy
Venue: arXiv cs.CR 2017
Year: 2017

The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach to anonymisation. In this paper we present ($k$,$ε$)-anonymisation, an approach that combines the $k$-anonymisation and $ε$-differential privacy models into a single coherent framework, providing privacy guarantees at least as strong as those offered by the individual models. Linking risks of less than 5\% are observed in experimental results, even with modest values of $k$ and $ε$. Our approach is shown to address well-known limitations of $k$-anonymity and $ε$-differential privacy and is validated in an extensive experimental campaign using openly available datasets.
Note on "The Complexity of Counting Surjective Homomorphisms and Compactions"
Venue: arXiv cs.CC 2017
Year: 2017

Focke, Goldberg, and Živný (arXiv 2017) prove a complexity dichotomy for the problem of counting surjective homomorphisms from a large input graph G without loops to a fixed graph H that may have loops. In this note, we give a short proof of a weaker result: Namely, we only prove the #P-hardness of the more general problem in which G may have loops. Our proof is an application of a powerful framework of Lovász (2012), and it is analogous to proofs of Curticapean, Dell, and Marx (STOC 2017) who studied the "dual" problem in which the pattern graph G is small and the host graph H is the input. Independently, Chen (arXiv 2017) used Lovász's framework to prove a complexity dichotomy for counting surjective homomorphisms to fixed finite structures.
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
Venue: arXiv cs.CV 2017
Year: 2017

Extracting, harvesting and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. It is also the bottleneck to designing more effective data-hungry computing paradigms (e.g., deep learning) for medical image analysis. Yet, vast amounts of clinical annotations (usually associated with disease image findings and marked using arrows, lines, lesion diameters, segmentation, etc.) have been collected over several decades and stored in hospitals' Picture Archiving and Communication Systems. In this paper, we mine and harvest one major type of clinical annotation data - lesion diameters annotated on bookmarked images - to learn an effective multi-class lesion detector via unsupervised and supervised deep Convolutional Neural Networks (CNN). Our dataset is composed of 33,688 bookmarked radiology images from 10,825 studies of 4,477 unique patients. For every bookmarked image, a bounding box is created to cover the target lesion based on its measured diameters. We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels. Next, we adopt a regional-CNN method to detect lesions of multiple categories, regardless of missing annotations (normally only one lesion is annotated, despite the presence of multiple co-existing findings). Our integrated mining, categorization and detection framework is validated with promising empirical results, as a scalable, universal or multi-purpose CAD paradigm built upon abundant retrospective medical data. Furthermore, we demonstrate that detection accuracy can be significantly improved by incorporating pseudo lesion labels (e.g., Liver lesion/tumor, Lung nodule/tumor, Abdomen lesions, Chest lymph node and others). This dataset will be made publicly available (under the open science initiative).
Enhanced Neural Machine Translation by Learning from Draft
Venue: arXiv cs.CL 2017
Year: 2017

Neural machine translation (NMT) has recently achieved impressive results. A potential problem of the existing NMT algorithm, however, is that the decoding is conducted from left to right, without considering the right context. This paper proposes an two-stage approach to solve the problem. In the first stage, a conventional attention-based NMT system is used to produce a draft translation, and in the second stage, a novel double-attention NMT system is used to refine the translation, by looking at the original input as well as the draft translation. This drafting-and-refinement can obtain the right-context information from the draft, hence producing more consistent translations. We evaluated this approach using two Chinese-English translation tasks, one with 44k pairs and 1M pairs respectively. The experiments showed that our approach achieved positive improvements over the conventional NMT system: the improvements are 2.4 and 0.9 BLEU points on the small-scale and large-scale tasks, respectively.
Automatic Taxonomy Generation - A Use-Case in the Legal Domain
Venue: arXiv cs.AI 2017
Year: 2017

A key challenge in the legal domain is the adaptation and representation of the legal knowledge expressed through texts, in order for legal practitioners and researchers to access this information easier and faster to help with compliance related issues. One way to approach this goal is in the form of a taxonomy of legal concepts. While this task usually requires a manual construction of terms and their relations by domain experts, this paper describes a methodology to automatically generate a taxonomy of legal noun concepts. We apply and compare two approaches on a corpus consisting of statutory instruments for UK, Wales, Scotland and Northern Ireland laws.
Diffusion Based Cooperative Molecular Communication in Nano-Networks
Venue: arXiv cs.IT 2017
Year: 2017

This work presents a novel diffusion based dual-phase molecular communication system where the source leverages multiple cooperating nanomachines to improve the end-to-end reliability of communication. The Neyman-Pearson Likelihood Ratio Tests are derived for each of the cooperative as well as the destination nanomachines in the presence of multi-user interference. Further, to characterize the performance of the aforementioned system, closed form expressions are derived for the probabilities of detection, false alarm at the individual cooperative, destination nanomachines, as well as the overall end-to-end probability of error. Simulation results demonstrate a significant improvement in the end-to-end performance of the proposed cooperative framework in comparison to multiple-input single-output and single-input single-output molecular communication scenarios in the existing literature.
Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics
Venue: arXiv cs.LG 2017
Year: 2017

Deep learning methods are used on spectroscopic data to predict drug content in tablets from near infrared (NIR) spectra. Using convolutional neural networks (CNNs), features are ex- tracted from the spectroscopic data. Extended multiplicative scatter correction (EMSC) and a novel spectral data augmentation method are benchmarked as preprocessing steps. The learned models perform better or on par with hypothetical optimal partial least squares (PLS) models for all combinations of preprocessing. Data augmentation with subsequent EMSC in combination gave the best results. The deep learning model CNNs also outperform the PLS models in an extrapolation chal- lenge created using data from a second instrument and from an analyte concentration not covered by the training data. Qualitative investigations of the CNNs kernel activations show their resemblance to wellknown data processing methods such as smoothing, slope/derivative, thresholds and spectral region selection.
Efficient Compression and Indexing of Trajectories
Venue: arXiv cs.DS 2017
Year: 2017

We present a new compressed representation of free trajectories of moving objects. It combines a partial-sums-based structure that retrieves in constant time the position of the object at any instant, with a hierarchical minimum-bounding-boxes representation that allows determining if the object is seen in a certain rectangular area during a time period. Combined with spatial snapshots at regular intervals, the representation is shown to outperform classical ones by orders of magnitude in space, and also to outperform previous compressed representations in time performance, when using the same amount of space.
Feedback Regularization and Geometric PID Control for Robust Stabilization of a Planar Three-link Hybrid Bipedal Walking Model
Venue: arXiv cs.SY 2017
Year: 2017

This paper applies a recently developed geometric PID controller to stabilize a three-link planar bipedal hybrid dynamic walking model. The three links represent the robot torso and two kneeless legs, with an independent control torque available at each hip joint. The geometric PID controller is derived for fully actuated mechanical systems, however in the swing phase the three-link biped robot has three degrees of freedom and only two controls. Following the bipedal walking literature, underactuation is addressed by choosing two "virtual constraints" to enforce, and verifying the stability of the resulting two-dimensional zero dynamics. The resulting controlled dynamics do not have the structure of a mechanical system, however this structure is restored using "feedback regularization," following which geometric PID control is used to provide robust asymptotic regulation of the virtual constraints. The proposed method can tolerate significantly greater variations in inclination, showing the value of the geometric methods, and the benefit of integral action.
Morphology Generation for Statistical Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

When translating into morphologically rich languages, Statistical MT approaches face the problem of data sparsity. The severity of the sparseness problem will be high when the corpus size of morphologically richer language is less. Even though we can use factored models to correctly generate morphological forms of words, the problem of data sparseness limits their performance. In this paper, we describe a simple and effective solution which is based on enriching the input corpora with various morphological forms of words. We use this method with the phrase-based and factor-based experiments on two morphologically rich languages: Hindi and Marathi when translating from English. We evaluate the performance of our experiments both in terms automatic evaluation and subjective evaluation such as adequacy and fluency. We observe that the morphology injection method helps in improving the quality of translation. We further analyze that the morph injection method helps in handling the data sparseness problem to a great level.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Venue: arXiv cs.CV 2017
Year: 2017

Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up. Existing multi-target tracking methods often use low-level features which are not sufficiently discriminative for identifying faces with such large appearance variations. In this paper, we tackle this problem by learning discriminative, video-specific face representations using convolutional neural networks (CNNs). Unlike existing CNN-based approaches which are only trained on large-scale face image datasets offline, we use the contextual constraints to generate a large number of training samples for a given video, and further adapt the pre-trained face CNN to specific videos using discovered training samples. Using these training samples, we optimize the embedding space so that the Euclidean distances correspond to a measure of semantic face similarity via minimizing a triplet loss function. With the learned discriminative features, we apply the hierarchical clustering algorithm to link tracklets across multiple shots to generate trajectories. We extensively evaluate the proposed algorithm on two sets of TV sitcoms and YouTube music videos, analyze the contribution of each component, and demonstrate significant performance improvement over existing techniques.
Real-Time Simulation and Hardware-in-the-Loop Approaches for Integrating Renewable Energy Sources into Smart Grids: Challenges & Actions
Venue: arXiv cs.SY 2017
Year: 2017

The integration of distributed renewable energy sources and the multi-domain behaviours inside the cyber-physical energy system (smart grids) draws up major challenges. Their validation and roll out requires careful assessment, in term of modelling, simulation and testing. The traditional approach focusing on a particular object, actual hardware or a detailed model, while drastically simplifying the remainder of the system under test, is no longer sufficient. Real-time simulation and Hardware-in-the-Loop (HIL) techniques emerge as indispensable tools for validating the behaviour of renewable sources as well as their impact/interaction to with the cyber-physical energy system. This paper aims to provide an overview of the present status-quo of real-time and HIL approaches used for smart grids and their readiness for cyber-physical experiments. We investigate the current limitations of HIL techniques and point out necessary future developments. Subsequently, the paper highlights challenges that need specific attention as well as ongoing actions and further research directions.
Human Pose Regression by Combining Indirect Part Detection and Contextual Information
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose an end-to-end trainable regression approach for human pose estimation from still images. We use the proposed Soft-argmax function to convert feature maps directly to joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two very challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods and comparable results to the state-of-the-art detection based approaches.
Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics
Venue: arXiv cs.CL 2017
Year: 2017

Lexical entailment, such as hyponymy, is a fundamental issue in the semantics of natural language. This paper proposes distributional semantic models which efficiently learn word embeddings for entailment, using a recently-proposed framework for modelling entailment in a vector-space. These models postulate a latent vector for a pseudo-phrase containing two neighbouring word vectors. We investigate both modelling words as the evidence they contribute about this phrase vector, or as the posterior distribution of a one-word phrase vector, and find that the posterior vectors perform better. The resulting word embeddings outperform the best previous results on predicting hyponymy between words, in unsupervised and semi-supervised experiments.
On the Challenges of Sentiment Analysis for Dynamic Events
Venue: arXiv cs.CL 2017
Year: 2017

With the proliferation of social media over the last decade, determining people's attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called sentiment and emotion analysis. For instance, businesses routinely look to develop systems to automatically understand their customer conversations by identifying the relevant content to enhance marketing their products and managing their reputations. Previous efforts to assess people's sentiment on Twitter have suggested that Twitter may be a valuable resource for studying political sentiment and that it reflects the offline political landscape. According to a Pew Research Center report, in January 2016 44 percent of US adults stated having learned about the presidential election through social media. Furthermore, 24 percent reported use of social media posts of the two candidates as a source of news and information, which is more than the 15 percent who have used both candidates' websites or emails combined. The first presidential debate between Trump and Hillary was the most tweeted debate ever with 17.1 million tweets.
Real-Time Illegal Parking Detection System Based on Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.